Re: How to find lead-lag relation in two time series?

The standard approach is to estimate a vector autoregression involving your
variables of interest and then test for Granger non-causality. See the vars
package and in particular the causality() function.

Re: How to find lead-lag relation in two time series?

You want to use returns, not prices.
Correlations with prices are spurious.
(The extreme example is to think of
a long set of series with inflation --
all the price series will be positively
correlated.)

On 19/02/2010 23:15, Michael Jungle wrote:

>
> One possibility is to do the cross-correlation.
>
> What series shall I apply cross-correlation to? Price or return series?
>
> If I do cross-correlation on two price series, and found some large
> correlation numbers,
>
> and then do cross-correlation on two return series, and found no significant
> numbers(almost zero),
>
> What does that mean?

Re: How to find lead-lag relation in two time series?

For lead and lags cross-correlation:

cc<-ccf(mdeaths, fdeaths,lag.max=4, plot=F)
cc
#but this function is rather ugly...

Patrick Burns a écrit :

> You want to use returns, not prices.
> Correlations with prices are spurious.
> (The extreme example is to think of
> a long set of series with inflation --
> all the price series will be positively
> correlated.)
>
>
> On 19/02/2010 23:15, Michael Jungle wrote:
>>
>> One possibility is to do the cross-correlation.
>>
>> What series shall I apply cross-correlation to? Price or return series?
>>
>> If I do cross-correlation on two price series, and found some large
>> correlation numbers,
>>
>> and then do cross-correlation on two return series, and found no
>> significant
>> numbers(almost zero),
>>
>> What does that mean?
>

Re: How to find lead-lag relation in two time series?

Price time series will usually have a positive drift and thus are
non-stationary.
As far as I know most methods of time series analysis deal with
stationary series and if you want to analyze a non-stationary series,
you should transform the series and obtain a stationary version of the
raw data first.
Return time series are usually (or at least assumed to be) stationary
and thus your focus should lie on returns rather than prices..

Hth
Thomas

Michael Jungle schrieb:

> Thx but why? I want buy/short based on price correlations right? Not returns...
>
> On Saturday, February 20, 2010, Patrick Burns-2 [via R]
> <[hidden email]> wrote:
>
>> You want to use returns, not prices.
>>
>> Correlations with prices are spurious.
>>
>> (The extreme example is to think of
>>
>> a long set of series with inflation --
>>
>> all the price series will be positively
>>
>> correlated.)
>>
>>
>>
>> On 19/02/2010 23:15, Michael Jungle wrote:
>>
>>
>>> One possibility is to do the cross-correlation.
>>>
>>> What series shall I apply cross-correlation to? Price or return series?
>>>
>>> If I do cross-correlation on two price series, and found some large
>>>
>>> correlation numbers,
>>>
>>> and then do cross-correlation on two return series, and found no significant
>>>
>>> numbers(almost zero),
>>>
>>> What does that mean?
>>>
>> --
>>
>> Patrick Burns
>>
>> [hidden email]Â <http://n4.nabble.com/user/SendEmail.jtp?type=node&node=1562668&i=0>
>>
>> http://www.burns-stat.com>>
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>
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Re: How to find lead-lag relation in two time series?

Thomas is right. One should never measure correlations based on prices-
it always leads to hugely inflated absolute correlations. It is not
uncommon for beginners to take correlations of prices and find, to their
delight, that correlations are as high as 70-90% on some assets. Then,
when they try to use spread option strategies to take advantage of the
tight correlations, they find that their strategies break down because
the actual correlations are only about 30-40%.

Re: How to find lead-lag relation in two time series?

Michael Jungle wrote:
> But we trade on prices, right? How do you trade on returns?
>
> My preliminary understanding is "whatever we trade on, we should find
> correlation, etc. there...".
>
> If you find lead-lag relations on returns, how do you trade them?
>
You've already been given the answer.

Correlations on prices produce spurious results.

Go do some research. The literature will agree with what you've been
told here.

Returns may be turned back into prices (or more appropriately, a wealth
index)